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# coding=utf-8 | |
# Copyright 2018 The Google AI Team Authors. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
# Lint as: python3 | |
"""Helper library for ALBERT fine-tuning. | |
This library can be used to construct ALBERT models for fine-tuning, either from | |
json config files or from TF-Hub modules. | |
""" | |
from albert import modeling | |
from albert import tokenization | |
import tensorflow.compat.v1 as tf | |
import tensorflow_hub as hub | |
def _create_model_from_hub(hub_module, is_training, input_ids, input_mask, | |
segment_ids): | |
"""Creates an ALBERT model from TF-Hub.""" | |
tags = set() | |
if is_training: | |
tags.add("train") | |
albert_module = hub.Module(hub_module, tags=tags, trainable=True) | |
albert_inputs = dict( | |
input_ids=input_ids, | |
input_mask=input_mask, | |
segment_ids=segment_ids) | |
albert_outputs = albert_module( | |
inputs=albert_inputs, | |
signature="tokens", | |
as_dict=True) | |
return (albert_outputs["pooled_output"], albert_outputs["sequence_output"]) | |
def _create_model_from_scratch(albert_config, is_training, input_ids, | |
input_mask, segment_ids, use_one_hot_embeddings, | |
use_einsum): | |
"""Creates an ALBERT model from scratch/config.""" | |
model = modeling.AlbertModel( | |
config=albert_config, | |
is_training=is_training, | |
input_ids=input_ids, | |
input_mask=input_mask, | |
token_type_ids=segment_ids, | |
use_one_hot_embeddings=use_one_hot_embeddings, | |
use_einsum=use_einsum) | |
return (model.get_pooled_output(), model.get_sequence_output()) | |
def create_albert(albert_config, is_training, input_ids, input_mask, | |
segment_ids, use_one_hot_embeddings, use_einsum, hub_module): | |
"""Creates an ALBERT, either from TF-Hub or from scratch.""" | |
if hub_module: | |
tf.logging.info("creating model from hub_module: %s", hub_module) | |
return _create_model_from_hub(hub_module, is_training, input_ids, | |
input_mask, segment_ids) | |
else: | |
tf.logging.info("creating model from albert_config") | |
return _create_model_from_scratch(albert_config, is_training, input_ids, | |
input_mask, segment_ids, | |
use_one_hot_embeddings, use_einsum) | |
def create_vocab(vocab_file, do_lower_case, spm_model_file, hub_module): | |
"""Creates a vocab, either from vocab file or from a TF-Hub module.""" | |
if hub_module: | |
use_spm = True if spm_model_file else False | |
return tokenization.FullTokenizer.from_hub_module( | |
hub_module=hub_module, use_spm=use_spm) | |
else: | |
return tokenization.FullTokenizer.from_scratch( | |
vocab_file=vocab_file, do_lower_case=do_lower_case, | |
spm_model_file=spm_model_file) | |